隨著生成式人工智慧的興起，這類的新科技已被運用在許多不同的領域當中，其中最常見的運用便是使用在聊天機器人中，添加生成式人工智慧下的聊天機器人能夠更加理解使用者所欲敘述之事情，也能自己生成新內容，因此不像以往的聊天機器人如此死板。本研究以正義理論探討擬人化生成式聊天機器人在服務補救情境下對使用者忠誠度的影響，並以ChatGPT為例。探討服務補救的策略如何減輕負面情緒，進而影響忠誠度。
本研究使用實驗以及問卷相互結合的方法完成資料的蒐集，受測者需依照給予的指示於ChatGPT進行服務補救過程。在實驗完後再進行填寫問卷。研究結果顯示分配正義（實質性）的補救方式比起互動正義（心理性）的補救方式更能夠緩解使用者負面情緒，而當ChatGPT能夠提供更多樣性的傳遞訊息時，使用者信任度與忠誠度也會更加提升，而當擬人化作用於此類的聊天機器人補救行為上，對於調節負面情緒是正向且會使得不同補救策略之間差異拉近。本研究提出結論以及建議，供後續生成式聊天機器人相關研究作為參考。

With the rise of generative artificial intelligence, such technologies have been applied in many different fields, with one of the most common applications being in customer service chatbots. Unlike traditional chatbots, those enhanced with generative AI can better understand what users intend to convey and can generate new content, thus making interactions less rigid. This study explores the impact of anthropomorphized generative chatbots, using ChatGPT as an example, on user loyalty in service recovery contexts from the perspective of justice theory. It examines how service recovery strategies can alleviate negative emotions and subsequently affect loyalty.
This research employs a combination of experiments and surveys to collect data. Participants are instructed to engage with ChatGPT for a service recovery process, followed by completing a questionnaire. The results indicate that distributive justice （substantive recovery） is more effective in alleviating users' negative emotions compared to interactional justice （psychological recovery）. Furthermore, when ChatGPT provides a more diverse message delivery to user, user trust and loyalty are also enhanced. However, anthropomorphism in such chatbot recovery actions has a positive effect on moderating negative emotions and narrows the differences between different recovery strategies. This study concludes with recommendations for future research on generative chatbots.

論文審定書 i
誌謝 ii
摘要 iii
Absract iv
圖次 vi
表次 vii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 2
第三節 研究目的及問題 3
第二章 文獻探討 4
第一節 生成式人工智慧 4
第二節 服務失效與服務補救 5
第三節 正義理論 7
第四節 人機互動 8
第三章 研究方法 10
第一節 研究模型 10
第二節 研究假說 10
第三節 操作型定義 13
第四節 研究設計 15
第四章 資料分析 19
第一節 敘述性統計分析 19
第二節 衡量模型 23
第三節 結構模型與假說驗證 32
第五章 結論與建議 35
第一節 研究結果與建議 35
第二節 研究貢獻 36
第三節 研究限制與未來研究方向 37
參考文獻 38
附錄一–本研究正式問卷 43
附錄二–本研究實驗情境 48

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